Background: Mechanical ventilation (MV) can induce pulmonary fibrosis. This study aims to investigate whether MV-induced pulmonary fibrosis is associated with aerobic glycolysis and seeks to uncover the underlying mechanisms mediated by integrin β3-pyruvate kinase M2 (PKM2) pathway. Methods: PKM2 knockdown or inhibition, integrin β3 knockout or inhibition and wild-type mice were exposed to MV (20 mL/kg) for 2 h. Results: Mice exposed to MV exhibited increased expression of collagen deposition, and upregulation of α-smooth muscle actin and collagen I in lung tissues. Single cells analysis showed that MV-induced pulmonary fibrosis was associated with increased gene expression of integrin and glycolysis in pulmonary fibroblasts, as well as upregulation of glycolytic products tested by metabolomics. Meanwhile, increased protein level of integrin β3 and PKM2 was confirmed by western blot and immunohistochemistry. Double immunofluorescence staining and flow cytometric analysis showed increased number of fibronectin+/integrin β3+ and fibronectin+/PKM2+ fibroblasts in lung tissues. Furthermore, MV-induced aerobic glycolysis and pulmonary fibrosis were ameliorated after treatment with PKM2 knockdown-AAV and inhibition, or in integrin β3 knockout and inhibition mice. Conclusions : Integrin β3-PKM2 pathway-mediated aerobic glycolysis contributes to MV-induced pulmonary fibrosis. The inhibition of aerobic glycolysis targeting integrin β3-PKM2 pathway may be a promising treatment for MV-induced pulmonary fibrosis.
Background: Acute kidney injury (AKI) is a prevalent and serious complication among patients with sepsis-associated acute respiratory distress syndrome (ARDS). Prompt and accurate prediction of AKI has an important role in timely intervention, ultimately improving the patients' survival rate. This study aimed to establish machine learning models to predict AKI via thorough analysis of data derived from electronic medical records. Method: The data of eligible patients were retrospectively collected from the Medical Information Mart for Intensive Care III database from 2001 to 2012. The primary outcome was the development of AKI within 48 hours after intensive care unit admission. Four different machine learning models were established based on logistic regression, support vector machine, random forest, and extreme gradient boosting (XGBoost). The performance of all predictive models was evaluated using the area under receiver operating characteristic curve, precision-recall curve, confusion matrix, and calibration plot. Moreover, the discrimination ability of the machine learning models was compared with that of the Sequential Organ Failure Assessment (SOFA) model. Results; Among 1,085 sepsis-associated ARDS patients included in this research, 375 patients (34.6%) developed AKI within 48 hours after intensive care unit admission. Twelve predictive variables were selected and further used to establish the machine learning models. The XGBoost model yielded the most accurate predictions with the highest area under receiver operating characteristic curve (0.86) and accuracy (0.81). In addition, a novel shiny application based on the XGBoost model was established to predict the probability of developing AKI among patients with sepsis-associated ARDS. Conclusions: Machine learning models could be used for predicting AKI in patients with sepsis-associated ARDS. Accordingly, a user-friendly shiny application based on the XGBoost model with reliable predictive performance was released online to predict the probability of developing AKI among patients with sepsis-associated ARDS.
BackgroundThe coronavirus disease 2019 (COVID-19) becomes a worldwide public health threat. Increasing evidence proves that COVID-19-induced acute injuries could be reversed by a couple of therapies. After that, post-COVID-19 fibrosis (PCF), a sequela of “Long COVID,” earns rapidly emerging concerns. PCF is associated with deteriorative lung function and worse quality of life. But the process of PCF remains speculative. Therefore, we aim to conduct a bibliometric analysis to explore the overall structure, hotspots, and trend topics of PCF.Materials and methodsA comprehensive search was performed in the Web of Science core database to collect literature on PCF. Search syntax included COVID-19 relevant terms: “COVID 19,” “COVID-19 Virus Disease,” “COVID-19 Virus Infection,” “Coronavirus Disease-19,” “2019 Novel Coronavirus Disease,” “2019 Novel Coronavirus Infection,” “SARS Coronavirus 2 Infection,” “COVID-19 Pandemic,” “Coronavirus,” “2019-nCoV,” and “SARS-CoV-2”; and fibrosis relevant terms: “Fibrosis,” “Fibroses,” and “Cirrhosis.” Articles in English were included. Totally 1,088 publications were enrolled. Searching results were subsequentially exported and collected for the bibliometric analysis. National, organizational, and individual level data were analyzed and visualized through biblioshiny package in the R, VOSviewer software, the CiteSpace software, and the Graphical Clustering Toolkit (gCLUTO) software, respectively.ResultsThe intrinsic structure and development in the field of PCF were investigated in the present bibliometric analysis. The topmost keywords were “COVID-19” (occurrences, 636) surrounded by “SARS-CoV-2” (occurrences, 242), “coronavirus” (occurrences, 123), “fibrosis” (occurrences, 120), and “pneumonia” (occurrences, 94). The epidemiology, physiopathology, diagnosis, and therapy of PCF were extensively studied. After this, based on dynamic analysis of keywords, hot topics sharply changed from “Wuhan,” “inflammation,” and “cytokine storm” to “quality of life” and “infection” through burst detection; from “acute respiratory syndrome,” “cystic-fibrosis” and “fibrosis” to “infection,” “COVID-19,” “quality-of-life” through thematic evolution; from “enzyme” to “post COVID.” Similarly, co-cited references analysis showed that topics of references with most citations shift from “pulmonary pathology” (cluster 0) to “COVID-19 vaccination” (cluster 6). Additionally, the overview of contributors, impact, and collaboration was revealed. Summarily, the USA stood out as the most prolific, influential, and collaborative country. The Udice French Research University, Imperial College London, Harvard University, and the University of Washington represented the largest volume of publications, citations, H-index, and co-authorships, respectively. Dana Albon was the most productive and cited author with the strongest co-authorship link strength. Journal of Cystic Fibrosis topped the list of prolific and influential journals.ConclusionOutcomes gained from this study assisted professionals in better realizing PCF and would guide future practices. Epidemiology, pathogenesis, and therapeutics were study hotspots in the early phase of PCF research. As the spread of the COVID-19 pandemic and progress in this field, recent attention shifted to the quality of life of patients and post-COVID comorbidities. Nevertheless, COVID-19 relevant infection and vaccination were speculated to be research trends with current and future interest. International cooperation as well as in-depth laboratory experiments were encouraged to promote further explorations in the field of PCF.
Recent clinical research has revealed that mechanical ventilation (MV) can initiate pulmonary fibrosis and induce mechanical ventilation-induced pulmonary fibrosis (MVPF). However, the underlying mechanism remains largely uncharacterized. Based on a mouse model of MVPF and an alveolar epithelial cell cyclic strain model, the present study explores the possible mechanism of MVPF. Single-cell RNA-sequencing and EV RNA-sequencing analysis revealed that MV promoted apoptosis signal-regulating kinase 1 (ASK1)-mediated endoplasmic reticulum (ER) stress pathway activation and extracellular vesicle (EV) release from alveolar epithelial cells. Furthermore, the ASK1-ER stress pathway was shown to mediate mechanical stretch (MS)- or MV-induced EV release and lung fibroblast activation in vivo and in vitro. These processes were suppressed by ER stress inhibitors or by silencing ASK1 with ASK1- short hairpin RNA (shRNA). In addition, MVPF was suppressed by inhibiting ASK1 and ER stress in vivo. Therefore, the present study demonstrates that ASK1-ER stress pathway-mediated fibrotic-EV release from alveolar epithelial cells contributes to fibroblast activation and the initiation of pulmonary fibrosis during MV. The inhibited release of EVs targeting the ASK1-ER stress pathway might be a promising treatment strategy for MVPF.
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